Certain deterministic nonlinear systems may show chaotic behavior. We consider the motion of qualitative information and the practicalities of extracting a part from chaotic experimental data. Our approach based on a ...Certain deterministic nonlinear systems may show chaotic behavior. We consider the motion of qualitative information and the practicalities of extracting a part from chaotic experimental data. Our approach based on a theorem of Takens draws on the ideas from the generalized theory of information known as singular system analysis. We illustrate this technique by numerical data from the chaotic region of the chaotic experimental data. The method of the singular-value decomposition is used to calculate the eigenvalues of embedding space matrix. The corresponding concrete algorithm to calculate eigenvectors and to obtain the basis of embedding vector space is proposed in this paper. The projection on the orthogonal basis generated by eigenvectors of timeseries data and concrete paradigm are also provided here. Meanwhile the state space reconstruction technology of different kinds of chaotic data obtained from dynamical system has also been discussed in detail.展开更多
The state space, reconstruction is the major important quantitative index for describing non-linear chaotic time series. Based on the work of many scholars, such as: AT. H. Packard, F. Takens, M. Casdagli, J. F. Gibso...The state space, reconstruction is the major important quantitative index for describing non-linear chaotic time series. Based on the work of many scholars, such as: AT. H. Packard, F. Takens, M. Casdagli, J. F. Gibson, CHEN Yu-shu et al, the state space was reconstructed using the method of Legendre coordinate. Several different scaling regimes for lag time tau were identified. The influence for state space reconstruction of lag time tau was discussed. The result tells us that is a good practical method for state space reconstruction.展开更多
Based on the Bayesian information criterion, this paper proposes the improved local linear prediction method to predict chaotic time series. This method uses spatial correlation and temporal correlation simultaneously...Based on the Bayesian information criterion, this paper proposes the improved local linear prediction method to predict chaotic time series. This method uses spatial correlation and temporal correlation simultaneously. Simulation results show that the improved local linear prediction method can effectively make multi-step and one-step prediction of chaotic time series and the multi-step prediction performance and one-step prediction accuracy of the improved local linear prediction method are superior to those of the traditional local linear prediction method.展开更多
The chaotic nonlinear time series method is applied to analyze the sliver irregularity in textile processing.Because it unifies the system's determinacy and randomness,it seems more adaptive to describe the sliver...The chaotic nonlinear time series method is applied to analyze the sliver irregularity in textile processing.Because it unifies the system's determinacy and randomness,it seems more adaptive to describe the sliver irregularity than conventional methods.Firstly,the chaos character,i.e.fractal dimension,positive Lyapunov exponent,and state space parameters,including time delay and reconstruction dimension,are calculated respectively.As a result,a positive Lyapunov exponent and a fractal dimension are obtained,which demonstrates that the system is chaotic in fact.Secondly,both local linear forecast and global forecast models based on the reconstructed state are adopted to predict a segment part of the sliver irregularity series,which proves the validity of this analysis.Therefore,the sliver irregularity series shows the evidence of chaotic phenomena,and thus laying the theoretical foundation for analyzing and modeling the sliver irregularity series by applying the chaos theory,and providing a new way to understand the complexity of the sliver irregularity much better.展开更多
基金The project supported by the National Natural Science Foundation of China(19672043)
文摘Certain deterministic nonlinear systems may show chaotic behavior. We consider the motion of qualitative information and the practicalities of extracting a part from chaotic experimental data. Our approach based on a theorem of Takens draws on the ideas from the generalized theory of information known as singular system analysis. We illustrate this technique by numerical data from the chaotic region of the chaotic experimental data. The method of the singular-value decomposition is used to calculate the eigenvalues of embedding space matrix. The corresponding concrete algorithm to calculate eigenvectors and to obtain the basis of embedding vector space is proposed in this paper. The projection on the orthogonal basis generated by eigenvectors of timeseries data and concrete paradigm are also provided here. Meanwhile the state space reconstruction technology of different kinds of chaotic data obtained from dynamical system has also been discussed in detail.
基金the National Natural Science Foundation of China(19990510)
文摘The state space, reconstruction is the major important quantitative index for describing non-linear chaotic time series. Based on the work of many scholars, such as: AT. H. Packard, F. Takens, M. Casdagli, J. F. Gibson, CHEN Yu-shu et al, the state space was reconstructed using the method of Legendre coordinate. Several different scaling regimes for lag time tau were identified. The influence for state space reconstruction of lag time tau was discussed. The result tells us that is a good practical method for state space reconstruction.
文摘Based on the Bayesian information criterion, this paper proposes the improved local linear prediction method to predict chaotic time series. This method uses spatial correlation and temporal correlation simultaneously. Simulation results show that the improved local linear prediction method can effectively make multi-step and one-step prediction of chaotic time series and the multi-step prediction performance and one-step prediction accuracy of the improved local linear prediction method are superior to those of the traditional local linear prediction method.
文摘The chaotic nonlinear time series method is applied to analyze the sliver irregularity in textile processing.Because it unifies the system's determinacy and randomness,it seems more adaptive to describe the sliver irregularity than conventional methods.Firstly,the chaos character,i.e.fractal dimension,positive Lyapunov exponent,and state space parameters,including time delay and reconstruction dimension,are calculated respectively.As a result,a positive Lyapunov exponent and a fractal dimension are obtained,which demonstrates that the system is chaotic in fact.Secondly,both local linear forecast and global forecast models based on the reconstructed state are adopted to predict a segment part of the sliver irregularity series,which proves the validity of this analysis.Therefore,the sliver irregularity series shows the evidence of chaotic phenomena,and thus laying the theoretical foundation for analyzing and modeling the sliver irregularity series by applying the chaos theory,and providing a new way to understand the complexity of the sliver irregularity much better.
